7,143 research outputs found
Modulated phases and devil's staircases in a layered mean-field version of the ANNNI model
We investigate the phase diagram of a spin- Ising model on a cubic
lattice, with competing interactions between nearest and next-nearest neighbors
along an axial direction, and fully connected spins on the sites of each
perpendicular layer. The problem is formulated in terms of a set of
noninteracting Ising chains in a position-dependent field. At low temperatures,
as in the standard mean-feild version of the Axial-Next-Nearest-Neighbor Ising
(ANNNI) model, there are many distinct spatially commensurate phases that
spring from a multiphase point of infinitely degenerate ground states. As
temperature increases, we confirm the existence of a branching mechanism
associated with the onset of higher-order commensurate phases. We check that
the ferromagnetic phase undergoes a first-order transition to the modulated
phases. Depending on a parameter of competition, the wave number of the striped
patterns locks in rational values, giving rise to a devil's staircase. We
numerically calculate the Hausdorff dimension associated with these
fractal structures, and show that increases with temperature but seems
to reach a limiting value smaller than .Comment: 17 pages, 6 figure
Replica-symmetric solutions of a dilute Ising ferromagnet in a random field
We use the replica method in order to obtain an expression for the
variational free energy of an Ising ferromagnet on a Viana-Bray lattice in the
presence of random external fields. Introducing a global order parameter, in
the replica-symmetric context, the problem is reduced to the analysis of the
solutions of a nonlinear integral equation. At zero temperature, and under some
restrictions on the form of the random fields, we are able to perform a
detailed analysis of stability of the replica-symmetric solutions. In contrast
to the behaviour of the Sherrington-Kirkpatrick model for a spin glass in a
uniform field, the paramagnetic solution is fully stable in a sufficiently
large random field
Polydispersity Effects in the Dynamics and Stability of Bubbling Flows
The occurrence of swarms of small bubbles in a variety of industrial systems
enhances their performance. However, the effects that size polydispersity may
produce on the stability of kinematic waves, the gain factor, mean bubble
velocity, kinematic and dynamic wave velocities is, to our knowledge, not yet
well established. We found that size polydispersity enhances the stability of a
bubble column by a factor of about 23% as a function of frequency and for a
particular type of bubble column. In this way our model predicts effects that
might be verified experimentally but this, however, remain to be assessed. Our
results reinforce the point of view advocated in this work in the sense that a
description of a bubble column based on the concept of randomness of a bubble
cloud and average properties of the fluid motion, may be a useful approach that
has not been exploited in engineering systems.Comment: 11 pages, 2 figures, presented at the 3rd NEXT-SigmaPhi International
Conference, 13-18 August, 2005, Kolymbari, Cret
3D human pose estimation from depth maps using a deep combination of poses
Many real-world applications require the estimation of human body joints for
higher-level tasks as, for example, human behaviour understanding. In recent
years, depth sensors have become a popular approach to obtain three-dimensional
information. The depth maps generated by these sensors provide information that
can be employed to disambiguate the poses observed in two-dimensional images.
This work addresses the problem of 3D human pose estimation from depth maps
employing a Deep Learning approach. We propose a model, named Deep Depth Pose
(DDP), which receives a depth map containing a person and a set of predefined
3D prototype poses and returns the 3D position of the body joints of the
person. In particular, DDP is defined as a ConvNet that computes the specific
weights needed to linearly combine the prototypes for the given input. We have
thoroughly evaluated DDP on the challenging 'ITOP' and 'UBC3V' datasets, which
respectively depict realistic and synthetic samples, defining a new
state-of-the-art on them.Comment: Accepted for publication at "Journal of Visual Communication and
Image Representation
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